1 files changed, 90 insertions, 1 deletions

diff --git a/src/client/cognitive_services/CognitiveServices.ts b/src/client/cognitive_services/CognitiveServices.ts
index 08fcb4883..d496b442e 100644
--- a/src/client/cognitive_services/CognitiveServices.ts
+++ b/src/client/cognitive_services/CognitiveServices.ts
@@ -6,12 +6,17 @@ import { RouteStore } from "../../server/RouteStore";
 import { Utils } from "../../Utils";
 import { InkData } from "../../new_fields/InkField";
 import { UndoManager } from "../util/UndoManager";
+import requestPromise = require("request-promise");
+import { List } from "../../new_fields/List";
+import { ClientRecommender } from "../ClientRecommender";
+import { ImageBox } from "../views/nodes/ImageBox";
 
 type APIManager<D> = { converter: BodyConverter<D>, requester: RequestExecutor };
 type RequestExecutor = (apiKey: string, body: string, service: Service) => Promise<string>;
 type AnalysisApplier<D> = (target: Doc, relevantKeys: string[], data: D, ...args: any) => any;
 type BodyConverter<D> = (data: D) => string;
 type Converter = (results: any) => Field;
+type TextConverter = (results: any, data: string) => Promise<{ keyterms: Field, external_recommendations: any, kp_string: string[] }>;
 
 export type Tag = { name: string, confidence: number };
 export type Rectangle = { top: number, left: number, width: number, height: number };
@@ -19,7 +24,8 @@ export type Rectangle = { top: number, left: number, width: number, height: numb
 export enum Service {
     ComputerVision = "vision",
     Face = "face",
-    Handwriting = "handwriting"
+    Handwriting = "handwriting",
+    Text = "text"
 }
 
 export enum Confidence {
@@ -219,4 +225,87 @@ export namespace CognitiveServices {
 
     }
 
+    export namespace Text {
+        export const Manager: APIManager<string> = {
+            converter: (data: string) => {
+                return JSON.stringify({
+                    documents: [{
+                        id: 1,
+                        language: "en",
+                        text: data
+                    }]
+                });
+            },
+            requester: async (apiKey: string, body: string, service: Service) => {
+                let serverAddress = "https://eastus.api.cognitive.microsoft.com";
+                let endpoint = serverAddress + "/text/analytics/v2.1/keyPhrases";
+                let sampleBody = {
+                    "documents": [
+                        {
+                            "language": "en",
+                            "id": 1,
+                            "text": "Hello world. This is some input text that I love."
+                        }
+                    ]
+                };
+                let actualBody = body;
+                const options = {
+                    uri: endpoint,
+                    body: actualBody,
+                    headers: {
+                        'Content-Type': 'application/json',
+                        'Ocp-Apim-Subscription-Key': apiKey
+                    }
+
+                };
+                console.log("requested!");
+                return request.post(options);
+            }
+        };
+
+        export namespace Appliers {
+
+            export async function vectorize(keyterms: any, dataDoc: Doc, mainDoc: boolean = false) {
+                console.log("vectorizing...");
+                //keyterms = ["father", "king"];
+
+                let args = { method: 'POST', uri: Utils.prepend("/recommender"), body: { keyphrases: keyterms }, json: true };
+                await requestPromise.post(args).then(async (wordvecs) => {
+                    if (wordvecs) {
+                        let indices = Object.keys(wordvecs);
+                        console.log("successful vectorization!");
+                        var vectorValues = new List<number>();
+                        indices.forEach((ind: any) => {
+                            //console.log(wordvec.word);
+                            vectorValues.push(wordvecs[ind]);
+                        });
+                        ClientRecommender.Instance.processVector(vectorValues, dataDoc, mainDoc);
+                    } // adds document to internal doc set
+                    else {
+                        console.log("unsuccessful :( word(s) not in vocabulary");
+                    }
+                    //console.log(vectorValues.size);
+                }
+                );
+            }
+
+            export const analyzer = async (dataDoc: Doc, target: Doc, keys: string[], data: string, converter: TextConverter, isMainDoc: boolean = false, isInternal: boolean = true) => {
+                let results = await ExecuteQuery(Service.Text, Manager, data);
+                console.log(results);
+                let { keyterms, external_recommendations, kp_string } = await converter(results, data);
+                target[keys[0]] = keyterms;
+                if (isInternal) {
+                    //await vectorize([data], dataDoc, isMainDoc);
+                    await vectorize(kp_string, dataDoc, isMainDoc);
+                } else {
+                    return external_recommendations;
+                }
+            };
+
+            // export async function countFrequencies() 
+        }
+
+    }
+
+
 }
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